ZIMBABWE COVID-19 DASHBOARD FOR GOOD HEALTH

#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++# # ZIMBABWE COVID-19 DASHBOARD FOR GOOD HEALTH ORGANISATION # # RESEARCHING ON COVID-19 IN ZIMBABWE IN COMPARISON TO OTHER COUNTRIES # #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++#

#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++# # GROUP 13 MEMBERS # # STUDENT NAMES REG NUMBER # # 1. STALYNE F. CHAHURUVA C21143902I # # 2. ISHMAEL G. KUTAMBURA C20143464L # # 3. ENERST ZIMUDZI C21143982O # # 4. CAROLINE NYAMUTSWA C21143960L # # 5. MISHECK MARENGA C21143570Q # # 6. VIMBAI MUSHORE C21143986L # # 7. DANIEL S. NEGANJE C21143676A # # 8. STANLEY CHIVANDIRE C15126377V # # 9. MALVERN T. CHIWARA C14122649M # #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++#

library(flexdashboard)

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library(ggplot2)

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library(tm)

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library(wordcloud)

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library(DT)
library(dplyr)

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library(tidyverse)

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library(tm)
library(ggplot2)
library(wordcloud)
library(flexdashboard)
library(broom)

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library(knitr)

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library(data.table)

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library(rgdal)

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library(sp)
library(DT)
library(rpivotTable)

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library(ggplot2)
library(leaflet)

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library(sf)

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library(grid)
library(tidyverse)
library(IRdisplay)

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library(plotly)

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library(dplyr)
library(ggpubr)

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library(openintro)

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library(highcharter)

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library(ggvis)

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library(fontawesome)

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# Load Shape
#library(readOGR)
base_data <- readOGR("C:\\Users\\Ishmael.Kutambura\\Desktop\\CUT EXAMS\\WEEKEND\\SEMESTER 1 LECTURES\\MSCDA604 - Data Visualisation Techniques\\Assignments\\Assignment 2\\Visual project\\TM_WORLD_BORDERS_SIMPL-0.3",
                     layer="TM_WORLD_BORDERS_SIMPL-0.3",verbose=FALSE)

# Load data
# Select World Data
base_data@data <- read.csv(file.choose())
data1 = base_data@data


# Color Palettes
# Death
mybins <- c(0,1000, 5000, 10000,50000, 100000, 200000, Inf)
mypalette <- colorBin( palette="YlOrBr", domain=data1$DEATH, na.color="transparent", bins=mybins)

# GDP
mybins1 <- c(0,1000, 5000, 10000,20000, 50000, 60000)
mypalette1 <- colorBin( palette="YlOrBr", domain=data1$GDP, na.color="transparent", bins=mybins1)


# Text popup:
mytext <- paste(
  "Country: ", data1$NAME,"<br/>", 
  "Area: ", data1$AREA, "<br/>",
  "Population: ", data1$POP, "<br/>",
  "GDP: ", data1$GDP, "<br/>",
  "Deaths: ", data1$DEATH, "<br/>",
  "Recovery Rate: ", round(data1$RECOVERED / data1$CONFIRMED * 100,1), "%",
  sep="") %>%
  lapply(htmltools::HTML)

# Choropleth map displaying divided geographical areas or regions that are coloured in relation to Covid deaths and GDP

# Using the leaflet package to build interactive map to enable zooming and hovering over a country to gets more details about it
m <- leaflet(base_data) %>% 
  addTiles()  %>% 
  setView( lat=-19, lng=30 , zoom=4) %>%
  addPolygons(
    fillColor = ~mypalette(DEATH), 
    stroke=TRUE, 
    fillOpacity = 0.9, 
    color="white", 
    weight=0.3,
    label = mytext,
    group = "Death",
    labelOptions = labelOptions( 
      style = list("font-weight" = "normal", padding = "5px 8px"), 
      textsize = "13px", 
      direction = "auto"
    ),
  ) %>%
  addLegend( pal=mypalette, values=~DEATH, opacity=0.9, title = "COVID Related Deaths ", position = "bottomleft", group = "Death") %>%
  addPolygons(
    fillColor = ~mypalette1(GDP), 
    stroke=TRUE, 
    fillOpacity = 0.9, 
    color="white", 
    weight=0.3,
    label = mytext,
    group = "GDP",
    labelOptions = labelOptions( 
      style = list("font-weight" = "normal", padding = "5px 8px"), 
      textsize = "13px", 
      direction = "auto"
    ),
  ) %>%
  addLegend( pal=mypalette, values=~GDP, opacity=0.9, title = "GDP ", position = "bottomright", group = "GDP" ) %>%
  addLayersControl(overlayGroups = c("Death", "GDP"), 
                 options = layersControlOptions(collapsed = F))
m

About Report

tags$h1("Zimbabwe COVID-19 Task Force was commissioned to inform the Zimbabwean populace  how the COVID-19 pandemic is spreading across the world. Whilst the comparison with the whole world was required, it was particularly important to share statistics within and among SADC countries. Better still, Covid-19 in Zimbabwe would be the gist of having this dashboard to see how as a country we are faring with this fatal disease. 
        
        Good Health, a research comapny decided to come up with a dashboard to assist with statistics on Covid-19 which the Zimbabwe Task Force would make use of for references. This is the beginning of the design of the Covid-19 dashboard")

Zimbabwe COVID-19 Task Force was commissioned to inform the Zimbabwean populace how the COVID-19 pandemic is spreading across the world. Whilst the comparison with the whole world was required, it was particularly important to share statistics within and among SADC countries. Better still, Covid-19 in Zimbabwe would be the gist of having this dashboard to see how as a country we are faring with this fatal disease. Good Health, a research comapny decided to come up with a dashboard to assist with statistics on Covid-19 which the Zimbabwe Task Force would make use of for references. This is the beginning of the design of the Covid-19 dashboard

# Library
library(leaflet)

# Create a color palette for the map:
mypalette <- colorNumeric( palette="viridis", domain=base_data@data$POP, na.color="transparent")
mypalette(c(45,43))

## [1] "#440154" "#440154"

# Basic choropleth with leaflet?
m <- leaflet(base_data) %>% 
  addTiles()  %>% 
  setView( lat=10, lng=0 , zoom=2) %>%
  addPolygons( fillColor = ~mypalette(POP), stroke=FALSE )

m

Visualize the numeric variable

# load ggplot2
library(ggplot2)

# Distribution of the population per country?
base_data@data %>% 
  ggplot( aes(x=as.numeric(POP))) + 
    geom_histogram(bins=20, fill='#69b3a2', color='white') +
    xlab("Population") + 
    theme_bw()

Change color scale

# Color by quantile
m <- leaflet(base_data)%>% addTiles()  %>% setView( lat=10, lng=0 , zoom=2) %>%
  addPolygons( stroke = FALSE, fillOpacity = 0.5, smoothFactor = 0.5, color = ~colorQuantile("YlOrRd", POP)(POP) )
m

# Numeric palette
m <- leaflet(base_data)%>% addTiles()  %>% setView( lat=10, lng=0 , zoom=2) %>%
  addPolygons( stroke = FALSE, fillOpacity = 0.5, smoothFactor = 0.5, color = ~colorNumeric("YlOrRd", POP)(POP) )
m

# Bin
m <- leaflet(base_data)%>% addTiles()  %>% setView( lat=10, lng=0 , zoom=2) %>%
  addPolygons( stroke = FALSE, fillOpacity = 0.5, smoothFactor = 0.5, color = ~colorBin("YlOrRd", POP)(POP) )
m

Customizied leaflet choropleth map

# Create a color palette with handmade bins.
library(RColorBrewer)
mybins <- c(0,10,20,50,100,500,Inf)
mypalette <- colorBin( palette="YlOrBr", domain=base_data@data$POP, na.color="transparent", bins=mybins)
 
# Prepare the text for tooltips:
mytext <- paste(
    "Country: ", base_data@data$NAME,"<br/>", 
    "Area: ", base_data@data$AREA, "<br/>", 
    "Population: ", round(base_data@data$POP, 2), 
    "GDP: ", base_data@data$GDP, "<br/>",
    "Deaths: ", base_data@data$DEATH, "<br/>",
    "Recovery Rate: ", round(base_data@data$RECOVERED / base_data@data$CONFIRMED * 100,1), "%",
    sep="") %>%
  lapply(htmltools::HTML)
 
# Final Choropleth map displaying divided geographical areas or regions that are coloured in relation to Covid deaths and GDP

# Using the leaflet package to build interactive map to enable zooming and hovering over a country to gets more details about it
m <- leaflet(base_data) %>% 
  addTiles()  %>% 
  setView( lat=-19, lng=30 , zoom=4) %>%
  addPolygons(
    fillColor = ~mypalette(DEATH), 
    stroke=TRUE, 
    fillOpacity = 0.9, 
    color="white", 
    weight=0.3,
    label = mytext,
    group = "Death",
    labelOptions = labelOptions( 
      style = list("font-weight" = "normal", padding = "5px 8px"), 
      textsize = "13px", 
      direction = "auto"
    ),
  ) %>%
  addLegend( pal=mypalette, values=~DEATH, opacity=0.9, title = "COVID Related Deaths ", position = "bottomleft", group = "Death") %>%
  addPolygons(
    fillColor = ~mypalette1(GDP), 
    stroke=TRUE, 
    fillOpacity = 0.9, 
    color="white", 
    weight=0.3,
    label = mytext,
    group = "GDP",
    labelOptions = labelOptions( 
      style = list("font-weight" = "normal", padding = "5px 8px"), 
      textsize = "13px", 
      direction = "auto"
    ),
  ) %>%
  addLegend( pal=mypalette, values=~GDP, opacity=0.9, title = "GDP ", position = "bottomright", group = "GDP" ) %>%
  addLayersControl(overlayGroups = c("Death", "GDP"), 
                 options = layersControlOptions(collapsed = F))
m

COVID 19 Analysis

valueBox(paste(""),
         color = "warning")

#Display values
death.zim <- data1 %>% filter(NAME == "Zimbabwe") %>% select(DEATH)
infected.zim <- data1 %>% filter(NAME == "Zimbabwe") %>% select(CONFIRMED)
recovered.zim <- data1 %>% filter(NAME == "Zimbabwe") %>% select(RECOVERED)

Data Effects Analysis

valueBox(infected.zim, caption = 'Total Infected', icon = 'fa fa-ambulance')

32952

Totally Destroyed

valueBox(recovered.zim, caption =  "Total Recoveries", icon = 'fa fa-smile-o')

24872

Partially Destroyed

valueBox(death.zim, caption =  "Total Fatalities", icon = "fa fa-close")

1178

Effects by SADC

#select SADC COVID
library(corrplot)

## Warning: package 'corrplot' was built under R version 4.0.5

## corrplot 0.84 loaded

sadc_csv <- read.csv(file.choose(), header = TRUE)
sadc <- as.data.frame(sadc_csv)
#Grouped
ggplot(sadc, aes(fill=Property, y=Value, x=Country)) + 
  geom_bar(position="dodge", stat="identity") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

## Warning: Removed 27 rows containing missing values (geom_bar).

Correlation of Factors

trimmed <- data1 %>% select(6:17)
corrplot(cor(trimmed), method = "circle")

Data Table for the Covid Dashboard ========================================

data1 %>%
  datatable(extensions = 'Buttons',
            options = list(dom = 'Blfrtip',
                           buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
                           lengthMenu = list(c(10,25,50,-1),
                                             c(10,25,50,"All"))))

Hexabinning the map of US using mutate

# library
library(tidyverse)
library(geojsonio)

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##   print.geojson geojson

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library(RColorBrewer)
library(rgdal)


# Load this file. (Note: I stored in a folder called DATA)
spdf <- geojson_read("World_Borders/us_states_hexgrid.json",  what = "sp")

# Bit of reformating
spdf@data = spdf@data %>%
  mutate(google_name = gsub(" \\(United States\\)", "", google_name))

# Show it
plot(spdf)

Hexabin with state name

# I need to 'fortify' the data to be able to show it with ggplot2 (we need a data frame format)
library(broom)
spdf@data = spdf@data %>% mutate(google_name = gsub(" \\(United States\\)", "", google_name))
spdf_fortified <- tidy(spdf, region = "google_name")

# Calculate the centroid of each hexagon to add the label:
library(rgeos)

## Warning: package 'rgeos' was built under R version 4.0.5

## rgeos version: 0.5-5, (SVN revision 640)
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##  Linking to sp version: 1.4-5 
##  Polygon checking: TRUE

centers <- cbind.data.frame(data.frame(gCentroid(spdf, byid=TRUE), id=spdf@data$iso3166_2))
 
# Now I can plot this shape easily as described before:
ggplot() +
  geom_polygon(data = spdf_fortified, aes( x = long, y = lat, group = group), fill="skyblue", color="white") +
  geom_text(data=centers, aes(x=x, y=y, label=id)) +
  theme_void() +
  coord_map()

Effects by SADC

#select SADC COVID
library(corrplot)
sadc_csv <- read.csv(file.choose(), header = TRUE)
sadc <- as.data.frame(sadc_csv)
#Grouped
ggplot(sadc, aes(fill=Property, y=Value, x=Country)) + 
  geom_bar(position="dodge", stat="identity") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

## Warning: Removed 27 rows containing missing values (geom_bar).

Cartogram

# Keep only data concerning Africa
library(maptools)

## Warning: package 'maptools' was built under R version 4.0.5

## Checking rgeos availability: TRUE

data(my_africa)

## Warning in data(my_africa): data set 'my_africa' not found

my_africa <- readOGR("C:\\Users\\Ishmael.Kutambura\\Desktop\\CUT EXAMS\\WEEKEND\\SEMESTER 1 LECTURES\\MSCDA604 - Data Visualisation Techniques\\Assignments\\Assignment 2\\Group 13 Project\\World_Borders",
                     layer="TM_WORLD_BORDERS_SIMPL-0.3",verbose=FALSE)

africa <- my_africa[base_data@data$REGION==2 , ]

# We can visualize the Africa's boundaries with the plot function
par(mar=c(0,0,0,0))
plot(africa , xlim=c(-20,60) , ylim=c(-40,35), col="steelblue", lwd=0.5 )

Simplifying geospatial object

#  simplify the geospatial object. Basically, it decreases the border precision which results in a lighter object that will be plotted faster.
# Load the rgeos library
library(rgeos)

africaSimple <- gSimplify(africa, tol = 4, topologyPreserve = TRUE)

# Plot it
par(mar=c(0,0,0,0))
plot(africaSimple , xlim=c(-20,60) , ylim=c(-40,35), col="#59b2a3", lwd=0.5 )

Computing the centroid of Africa

# The gCentroid function computes the centroid of each region:
# gCentroid(africa, byid=TRUE)

# selecting countries with area above 20000
africaBig <- africa[which(africa@data$AREA>20000), ]

# Small manipulation to put it in a dataframe:
centers <- cbind.data.frame(data.frame(gCentroid(africaBig, byid=TRUE), id=africaBig@data$FIPS))

# Show it on the map?
par(mar=c(0,0,0,0))
plot(africa , xlim=c(-20,60) , ylim=c(-40,35), lwd=0.5 )
text(centers$x, centers$y, centers$id, cex=.9, col="#69b3a2")

Interactive bubble maps with leaflet

# Load libraries
library(leaflet)

# Make data with several positions
data_red <- data.frame(LONG=42+rnorm(10), LAT=23+rnorm(10), PLACE=paste("Red_place_",seq(1,10)))
data_blue <- data.frame(LONG=42+rnorm(10), LAT=23+rnorm(10), PLACE=paste("Blue_place_",seq(1,10)))

# Initialize the leaflet map:
m <- leaflet() %>% 
  setView(lng=42, lat=23, zoom=6 ) %>%

  # Add two tiles
  addProviderTiles("Esri.WorldImagery", group="background 1") %>%
  addTiles(options = providerTileOptions(noWrap = TRUE), group="background 2") %>%

  # Add 2 marker groups
  addCircleMarkers(data=data_red, lng=~LONG , lat=~LAT, radius=8 , color="black",
                   fillColor="red", stroke = TRUE, fillOpacity = 0.8, group="Red") %>%
  addCircleMarkers(data=data_blue, lng=~LONG , lat=~LAT, radius=8 , color="black",
                   fillColor="blue", stroke = TRUE, fillOpacity = 0.8, group="Blue") %>%

  # Add the control widget
  addLayersControl(overlayGroups = c("Red","Blue") , baseGroups = c("background 1","background 2"), 
                   options = layersControlOptions(collapsed = FALSE))

m

Bubble Map

# Libraries
library(ggplot2)
library(dplyr)
 
# Get the world polygon and extract UK
library(maps)

## Warning: package 'maps' was built under R version 4.0.5

## 
## Attaching package: 'maps'

## The following object is masked from 'package:purrr':
## 
##     map

UK <- map_data("world") %>% filter(region=="UK")

# Get a data frame with longitude, latitude, and size of bubbles (a bubble = a city)
data <- world.cities %>% filter(country.etc=="UK")

scatterplot map

# Left chart
ggplot() +
  geom_polygon(data = UK, aes(x=long, y = lat, group = group), fill="grey", alpha=0.3) +
  geom_point( data=data, aes(x=long, y=lat)) +
  theme_void() + ylim(50,59) + coord_map() 

# Second graphic with names of the 10 biggest cities
library(ggrepel)

## Warning: package 'ggrepel' was built under R version 4.0.5

ggplot() +
  geom_polygon(data = UK, aes(x=long, y = lat, group = group), fill="grey", alpha=0.3) +
  geom_point( data=data, aes(x=long, y=lat, alpha=pop)) +
  geom_text_repel( data=data %>% arrange(pop) %>% tail(10), aes(x=long, y=lat, label=name), size=5) +
  geom_point( data=data %>% arrange(pop) %>% tail(10), aes(x=long, y=lat), color="red", size=3) +
  theme_void() + ylim(50,59) + coord_map() +
  theme(legend.position="none")

### Basic Bubble Map

# virids package for the color palette
library(viridis)

## Warning: package 'viridis' was built under R version 4.0.5

## Loading required package: viridisLite

## Warning: package 'viridisLite' was built under R version 4.0.5

# Left: use size and color
ggplot() +
  geom_polygon(data = UK, aes(x=long, y = lat, group = group), fill="grey", alpha=0.3) +
  geom_point( data=data, aes(x=long, y=lat, size=pop, color=pop)) +
  scale_size_continuous(range=c(1,12)) +
  scale_color_viridis(trans="log") +
  theme_void() + ylim(50,59) + coord_map() 

# Center: reorder your dataset first! Big cities appear later = on top
data %>%
 arrange(pop) %>% 
 mutate( name=factor(name, unique(name))) %>% 
 ggplot() +
    geom_polygon(data = UK, aes(x=long, y = lat, group = group), fill="grey", alpha=0.3) +
    geom_point( aes(x=long, y=lat, size=pop, color=pop), alpha=0.9) +
    scale_size_continuous(range=c(1,12)) +
    scale_color_viridis(trans="log") +
    theme_void() + ylim(50,59) + coord_map() + theme(legend.position="none")

# Right: just use arrange(desc(pop)) instead
data %>%
 arrange(desc(pop)) %>% 
 mutate( name=factor(name, unique(name))) %>% 
 ggplot() +
    geom_polygon(data = UK, aes(x=long, y = lat, group = group), fill="grey", alpha=0.3) +
    geom_point( aes(x=long, y=lat, size=pop, color=pop), alpha=0.9) +
    scale_size_continuous(range=c(1,12)) +
    scale_color_viridis(trans="log") +
    theme_void() + ylim(50,59) + coord_map() + theme(legend.position="none")

Customised Bubble Map

# Create breaks for the color scale
mybreaks <- c(0.02, 0.04, 0.08, 1, 7)

# Reorder data to show biggest cities on top
data <- data %>%
  arrange(pop) %>%
  mutate( name=factor(name, unique(name))) %>%
  mutate(pop=pop/1000000) 

# Build the map
data %>%
  ggplot() +
    geom_polygon(data = UK, aes(x=long, y = lat, group = group), fill="grey", alpha=0.3) +
    geom_point(  aes(x=long, y=lat, size=pop, color=pop, alpha=pop), shape=20, stroke=FALSE) +
    scale_size_continuous(name="Population (in M)", trans="log", range=c(1,12), breaks=mybreaks) +
    scale_alpha_continuous(name="Population (in M)", trans="log", range=c(0.1, .9), breaks=mybreaks) +
    scale_color_viridis(option="magma", trans="log", breaks=mybreaks, name="Population (in M)" ) +
    theme_void() + ylim(50,59) + coord_map() + 
    guides( colour = guide_legend()) +
    ggtitle("The 1000 biggest cities in the UK") +
    theme(
      legend.position = c(0.85, 0.8),
      text = element_text(color = "#22211d"),
      plot.background = element_rect(fill = "#f5f5f2", color = NA), 
      panel.background = element_rect(fill = "#f5f5f2", color = NA), 
      legend.background = element_rect(fill = "#f5f5f2", color = NA),
      plot.title = element_text(size= 16, hjust=0.1, color = "#4e4d47", margin = margin(b = -0.1, t = 0.4, l = 2, unit = "cm")),
    )

### Interactive bubble map with plotly

# Load the plotly package
library(plotly)
 
# Rorder data + Add a new column with tooltip text
data <- data %>%
  arrange(pop) %>%
  mutate( name=factor(name, unique(name))) %>%
  mutate( mytext=paste(
    "City: ", name, "\n", 
    "Population: ", pop, sep="")
  )
 
# Make the map (static)
p <- data %>%
  ggplot() +
    geom_polygon(data = UK, aes(x=long, y = lat, group = group), fill="grey", alpha=0.3) +
    geom_point(aes(x=long, y=lat, size=pop, color=pop, text=mytext, alpha=pop) ) +
    scale_size_continuous(range=c(1,15)) +
    scale_color_viridis(option="inferno", trans="log" ) +
    scale_alpha_continuous(trans="log") +
    theme_void() +
    ylim(50,59) +
    coord_map() +
    theme(legend.position = "none")

## Warning: Ignoring unknown aesthetics: text

p <- ggplotly(p, tooltip="text")
p

### Data Table
install.packages("DT")

## Warning: package 'DT' is in use and will not be installed

library(DT)

datatable(data1)